Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Mining multiple-level spatial association rules for objects with a broad boundary
Data & Knowledge Engineering
ACM Transactions on Information Systems (TOIS)
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
On Mining General Temporal Association Rules in a Publication Database
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Safely delegating data mining tasks
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
A novel approach for discovering retail knowledge with price information from transaction databases
Expert Systems with Applications: An International Journal
Efficient clustering of databases induced by local patterns
Decision Support Systems
Context-based market basket analysis in a multiple-store environment
Decision Support Systems
Blind paraunitary equalization
Signal Processing
Expert Systems with Applications: An International Journal
A new sampling technique for association rule mining
Journal of Information Science
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
Electronic Commerce Research and Applications
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
Designing of dynamic labor inspection system for construction industry
Expert Systems with Applications: An International Journal
A product network analysis for extending the market basket analysis
Expert Systems with Applications: An International Journal
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Data mining office behavioural information from simple sensors
AUIC '12 Proceedings of the Thirteenth Australasian User Interface Conference - Volume 126
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Market basket analysis (also known as association-rule mining) is a useful method of discovering customer purchasing patterns by extracting associations or co-occurrences from stores' transactional databases. Because the information obtained from the analysis can be used in forming marketing, sales, service, and operation strategies, it has drawn increased research interest. The existing methods, however, may fail to discover important purchasing patterns in a multi-store environment, because of an implicit assumption that products under consideration are on shelf all the time across all stores. In this paper, we propose a new method to overcome this weakness. Our empirical evaluation shows that the proposed method is computationally efficient, and that it has advantage over the traditional method when stores are diverse in size, product mix changes rapidly over time, and larger numbers of stores and periods are considered.